Cherkassky V, Ma Y(2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. , 17, 113-126.
Gaudes CC, Santamaria I, Javier V, Masgrau E, Sese T(2007). Robust array beamforming with sidelobe control using support vector machines. IEEE Trans. Sig. Proc., 55(2), 574-584.
Gretton A, Herbrich R, Smola A (2003). The Kernel mutual information. Max Planck Institute for Biological Cybernetics.
Mestre X, Lagunas MA (2006). Finite sample size effect on minimum variance beamformers: Optimum diagonal loading factor for large arrays. IEEE Trans. Signal Process, 54(1), 69-82.
Mitra P, Murthy C, Pal S (2004). A probabilistic active support vector learning algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3), 413-418.
Ramon MM, Xu Nan, Christodoulou CG (2005). Beamforming using support vector machines. IEEE Antennas and Wireless Propagation Letters, 4, 439-442.
Smola AJ, Schölkopf B (2003). A tutorial on support vector regression. Royal Holloway College, Univ. London, U.K, NeuroCOLT Tech. Rep.NC-TR-98-030.
Song Xin, Wang Jinkuan, Wang Bin, Han Yinghua (2009). Robust adaptive beamforming algorithm under nonlinear constraint. IEEE Int. Conf. Mechatronics Autom., ICMA, 2989-2993.
Trafalis TB, Gilbert RC (2006). Robust classification and regression using support vector machines. Eur. J. Oper. Res., 173(3), 893-909.
Vapnik V (2001). Statistical learning theory. 2nd ed. Springer, New York.
Van Trees HL (2002). Detection, estimation, and modulation theory, part IV: optimum array processing. Wiley, New York.